Download to read offline
![[第35回 Machine Learning 15minutes!] Microsoft AI Updates](https://cdn.slidesharecdn.com/ss_thumbnails/20190427ml15minmicrosoftai-190427060706-thumbnail.jpg?width=640&height=640&fit=bounds)
AML_service.pptx
- 1. LearnAI - Machine Learningon Azure LearnAI@Microsoft.com Seth Mottaghinejad Wolfgang Pauli, PhD
- 2. Overview ● Machine Learningon Azure ● Custom AI ● Compute Targets (DSVMs and Managed Compute) ● DevOps for Machine Learning ● Azure Machine Learning Pipelines ● Flexible and Support for Open Source Frameworks ● Deployment ● Tool Agnostic Python SDK
- 3. Building blocks fora Data Science Project Data sources Classical ML Deep learning Build and train models Experimentation and pipelines Hyperparameter tuning DevOps for data science Deployment
- 4. Machine Learning onAzure Domain Specific Pretrained Models To reduce time to market Azure Databricks Machine Learning VMs Popular Frameworks To build machine learning and deep learning solutions TensorFlow PyTorch ONNX Azure Machine Learning Language Speech … Search Vision Productive Services To empower data science and development teams Powerful Hardware To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science Tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
- 5. Azure Machine LearningService Set of Azure Cloud Services Python SDK ✓ Prepare Data ✓ Build Models ✓ Train Models ✓ Manage Models ✓ Track Experiments ✓ Deploy Models That enables you to:
- 6.
- 7. Productive Services Empower datascience and development teams Individual data scientists Desktop solutions adequate Need cloud for sporadic compute needs Integrated data science & data engineering teams Desktop solutions not adequate Need a unified big data & machine learning solution Azure Machine Learning service Azure Databricks + Machine Learning VMs
- 8. Data Science VirtualMachine
- 9. Data Science Virtual Machines (DSVM) Pre-Configuredenvironments in the cloud for Data Science & AI Modeling, Development & Deployment. Samples to get started
- 10.
- 11. Distributed training onmanaged compute Gather results Secure Access Scale resources Schedule jobs Dependencies and Containers Provision VM clusters Distribute data Handling failures
- 12. Training Infrastructure Scale resources Autoscaleresources to only pay while running a job Schedule jobs Train at cloud scale using a framework of choice Dependencies and Containers Leverage system-managed AML compute or bring your own compute Provision VM clusters Use the latest NDv2 series VMs with the NVIDIA V100 GPUs Distribute data Manage and share resources across a workspace
- 13. Powerful Infrastructure Accelerate deeplearning General purpose machine learning D, F, L, M, H Series CPUs Optimized for flexibility Optimized for performance GPUs FPGAs Deep learning N Series Specialized hardware accelerated deep learning Project Brainwave Support for image classification and recognition scenarios ResNet 50, ResNet 152, VGG-16, SSD-VGG, DenseNet-121 FPGA NEW UPDATES:
- 14. DevOps for MachineLearning
- 15. Prepare Data Register and Manage Model Train& Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy DevOps loop for data science
- 16. 0101010010100101110110100100100101101010 010001000111101001010101001010010101001 010010111011010010010010110101001000100 0111101001010101001010001010010111011010 010010010110101001000100011110100101010 100101001010100101001011101101001 DevOps loop fordata science Prepare Data Prepare Register and Manage Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model
- 17. Model Management indetail Create/Retrain Model Enable DevOps with full CI/CD integration with VSTS Register Model Track model versions with a central model registry Monitor Oversea deployments through Azure AppInsights
- 18. Use leaderboards, sideby side run comparison and model selection Conduct a hyperparameter search on traditional ML or DNN Leverage service-side capture of run metrics, output logs and models Manage training jobs locally, scaled-up or scaled-out Experimentation 95% 80% 75% 90% 85%
- 19. Azure Machine LearningPipelines
- 20. Azure Machine LearningPipelines Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring
- 21. Prepare data Build& train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Testing error Azure Machine Learning Pipelines
- 22. Prepare data Build& train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Testing error Model testing Model validation Deployment Batch scoring Error resolved Azure Machine Learning Pipelines
- 23. Azure Machine LearningPipelines with new data Prepare data Build & train models Deploy & predict Data storage locations Data ingestion Data Preparation Model building & training Model deployment Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation Deployment Batch scoring Normalization Transformation Validation Featurization Hyper-parameter tuning Automatic model selection Model testing Model validation New data Deployment Batch scoring
- 24. Advantages of AzureML Pipelines Unattended runs Schedule a few steps to run in parallel or in sequence to focus on other tasks while your pipeline runs Mixed and diverse compute Use multiple pipelines that are reliably coordinated across heterogeneous and scalable computes and storages Reusability Create templates of pipelines for specific scenarios such as retraining and batch scoring Tracking and versioning Name and version your data sources, inputs and outputs with the pipelines SDK
- 25. Support for Open sourceframeworks
- 26. Popular Frameworks Use yourfavorite machine learning frameworks without getting locked into one framework ONNX Community project created by Facebook and Microsoft Use the best tool for the job. Train in one framework and transfer to another for inference TensorFlow PyTorch Scikit-Learn MXNet Chainer Keras
- 27. Frameworks Create Native support Converters Services Azure Custom Vision Service Native support OtherDevices (iOS, etc) ML.NE T Azure Windows Server 2019 VM Azure Machine Learning services Ubuntu VM Deploy Windows Devices ONNX Model Native support Converters
- 28.
- 29. Flexible Deployment Deploy andmanage models on intelligent cloud and edge Train & deploy Train & deploy Deploy Model optimization for cloud & edge Manage models in production Capture model telemetry Retrain models
- 30. Deploy Azure MLmodels at scale Azure Machine Learning Service Azure Machine Learning Experimentation Cognitive Services External Model Model Registry Register Model Cloud Service Heavy Edge Light Edge Image Registry Create & Register Image Your IDE Scoring File Image Registry Deployment & Model Monitoring
- 31. Deployments to ComputeTargets
- 32.
- 33. Tool Agnostic PythonSDK Use your favorite IDEs, editors, notebooks, and frameworks Flexibility of your local environment or curated cloud environment Integrate with other services like Azure Databricks Get started quickly without any complex pre-requisites PyCharm Jupyter Visual Studio Code
- 34.
- 35. Azure Machine Learningservice Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere Tool agnostic Python SDK
- 36. Azure Machine Learningwith Azure Databricks https://aka.ms/aml-notebook-databricks-e2e Azure Notebooks https://notebooks.azure.com/azureml/projects/azureml-getting-started Azure ML Docs https://docs.microsoft.com/en-us/azure/machine-learning/service/ Resources beyond this AI Airlift
- 37.
- 38. Azure Subscriptions https://aka.ms/learnAIsubscriptions Azure DatabricksNotebooks https://aka.ms/learnAInotebooks.dbc Git Repository for LearnAI CustomAI Partner Airlift https://github.com/azure/learnai_azure_ml Resources for this Airlift